IDEAS home Printed from https://ideas.repec.org/a/plo/pcbi00/1003939.html
   My bibliography  Save this article

Statistical Computations Underlying the Dynamics of Memory Updating

Author

Listed:
  • Samuel J Gershman
  • Angela Radulescu
  • Kenneth A Norman
  • Yael Niv

Abstract

Psychophysical and neurophysiological studies have suggested that memory is not simply a carbon copy of our experience: Memories are modified or new memories are formed depending on the dynamic structure of our experience, and specifically, on how gradually or abruptly the world changes. We present a statistical theory of memory formation in a dynamic environment, based on a nonparametric generalization of the switching Kalman filter. We show that this theory can qualitatively account for several psychophysical and neural phenomena, and present results of a new visual memory experiment aimed at testing the theory directly. Our experimental findings suggest that humans can use temporal discontinuities in the structure of the environment to determine when to form new memory traces. The statistical perspective we offer provides a coherent account of the conditions under which new experience is integrated into an old memory versus forming a new memory, and shows that memory formation depends on inferences about the underlying structure of our experience.Author Summary: When do we modify old memories, and when do we create new ones? We suggest that this question can be answered statistically: The parsing of experience into distinct memory traces corresponds to inferences about the underlying structure of the environment. When sensory data change gradually over time, the brain infers that the environment has slowly been evolving, and the current representation of the environment (an existing memory trace) is updated. In contrast, abrupt changes indicate transitions between different structures, leading to the formation of new memories. While these ideas fall naturally out of statistical models of learning, they have not yet been directly tested in the domain of human memory. In this paper, we describe a model of statistical inference that instantiates these ideas, and test the model by asking human participants to reconstruct previously seen visual objects that have since changed gradually or abruptly. The results of this experiment support our theory of how the statistical structure of sensory experiences shapes memory formation.

Suggested Citation

  • Samuel J Gershman & Angela Radulescu & Kenneth A Norman & Yael Niv, 2014. "Statistical Computations Underlying the Dynamics of Memory Updating," PLOS Computational Biology, Public Library of Science, vol. 10(11), pages 1-13, November.
  • Handle: RePEc:plo:pcbi00:1003939
    DOI: 10.1371/journal.pcbi.1003939
    as

    Download full text from publisher

    File URL: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1003939
    Download Restriction: no

    File URL: https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1003939&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pcbi.1003939?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Robert C Wilson & Matthew R Nassar & Joshua I Gold, 2013. "A Mixture of Delta-Rules Approximation to Bayesian Inference in Change-Point Problems," PLOS Computational Biology, Public Library of Science, vol. 9(7), pages 1-18, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. He A Xu & Alireza Modirshanechi & Marco P Lehmann & Wulfram Gerstner & Michael H Herzog, 2021. "Novelty is not surprise: Human exploratory and adaptive behavior in sequential decision-making," PLOS Computational Biology, Public Library of Science, vol. 17(6), pages 1-32, June.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mel W Khaw & Luminita Stevens & Michael Woodford, 2021. "Individual differences in the perception of probability," PLOS Computational Biology, Public Library of Science, vol. 17(4), pages 1-25, April.
    2. Florent Meyniel, 2020. "Brain dynamics for confidence-weighted learning," PLOS Computational Biology, Public Library of Science, vol. 16(6), pages 1-27, June.
    3. Benjamin Skerritt-Davis & Mounya Elhilali, 2018. "Detecting change in stochastic sound sequences," PLOS Computational Biology, Public Library of Science, vol. 14(5), pages 1-24, May.
    4. Robert C Wilson & Yael Niv, 2015. "Is Model Fitting Necessary for Model-Based fMRI?," PLOS Computational Biology, Public Library of Science, vol. 11(6), pages 1-21, June.
    5. Elyse H Norton & Luigi Acerbi & Wei Ji Ma & Michael S Landy, 2019. "Human online adaptation to changes in prior probability," PLOS Computational Biology, Public Library of Science, vol. 15(7), pages 1-26, July.
    6. Payam Piray & Nathaniel D. Daw, 2021. "A model for learning based on the joint estimation of stochasticity and volatility," Nature Communications, Nature, vol. 12(1), pages 1-16, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pcbi00:1003939. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ploscompbiol (email available below). General contact details of provider: https://journals.plos.org/ploscompbiol/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.